{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6b025d6",
   "metadata": {},
   "source": [
    "# Bulk functional analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d9ebac",
   "metadata": {},
   "source": [
    "Bulk RNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by inferring pathway and transcription factor activities from prior knowledge.\n",
    "\n",
    "In this notebook we showcase how to use `decoupler` for transcription factor (TF) and pathway activity inference from a human data-set. The data consists of 6 samples of hepatic stellate cells (HSC) where three of them were activated by the cytokine Transforming growth factor (TGF-β), it is available at GEO [here](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151251).\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "This tutorial assumes that you already know the basics of `decoupler`. Else, check out the [Usage](https://decoupler-py.readthedocs.io/en/latest/notebooks/usage.html) tutorial first.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36d324eb",
   "metadata": {},
   "source": [
    "## Loading packages\n",
    "\n",
    "First, we need to load the relevant packages, `scanpy` to handle RNA-seq data\n",
    "and `decoupler` to use statistical methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "20d55bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import decoupler as dc\n",
    "\n",
    "# Only needed for processing\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from anndata import AnnData"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47b662e1",
   "metadata": {},
   "source": [
    "## Loading the data\n",
    "\n",
    "We can download the data easily from GEO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4651c698",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2023-06-01 11:29:28--  https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz\n",
      "Resolving www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)... 130.14.29.110, 2607:f220:41e:4290::110\n",
      "Connecting to www.ncbi.nlm.nih.gov (www.ncbi.nlm.nih.gov)|130.14.29.110|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1578642 (1,5M) [application/octet-stream]\n",
      "Saving to: ‘counts.txt.gz’\n",
      "\n",
      "counts.txt.gz       100%[===================>]   1,50M  2,14MB/s    in 0,7s    \n",
      "\n",
      "2023-06-01 11:29:29 (2,14 MB/s) - ‘counts.txt.gz’ saved [1578642/1578642]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget 'https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE151251&format=file&file=GSE151251%5FHSCs%5FCtrl%2Evs%2EHSCs%5FTGFb%2Ecounts%2Etsv%2Egz' -O counts.txt.gz\n",
    "!gzip -d -f counts.txt.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01794f8c",
   "metadata": {},
   "source": [
    "We can then read it using `pandas`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "43f47b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GeneName</th>\n",
       "      <th>DDX11L1</th>\n",
       "      <th>WASH7P</th>\n",
       "      <th>MIR6859-1</th>\n",
       "      <th>MIR1302-11</th>\n",
       "      <th>MIR1302-9</th>\n",
       "      <th>FAM138A</th>\n",
       "      <th>OR4G4P</th>\n",
       "      <th>OR4G11P</th>\n",
       "      <th>OR4F5</th>\n",
       "      <th>RP11-34P13.7</th>\n",
       "      <th>...</th>\n",
       "      <th>MT-ND4</th>\n",
       "      <th>MT-TH</th>\n",
       "      <th>MT-TS2</th>\n",
       "      <th>MT-TL2</th>\n",
       "      <th>MT-ND5</th>\n",
       "      <th>MT-ND6</th>\n",
       "      <th>MT-TE</th>\n",
       "      <th>MT-CYB</th>\n",
       "      <th>MT-TT</th>\n",
       "      <th>MT-TP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25_HSCs-Ctrl1</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>...</td>\n",
       "      <td>93192</td>\n",
       "      <td>342</td>\n",
       "      <td>476</td>\n",
       "      <td>493</td>\n",
       "      <td>54466</td>\n",
       "      <td>17184</td>\n",
       "      <td>1302</td>\n",
       "      <td>54099</td>\n",
       "      <td>258</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_HSCs-Ctrl2</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66</td>\n",
       "      <td>...</td>\n",
       "      <td>114914</td>\n",
       "      <td>355</td>\n",
       "      <td>388</td>\n",
       "      <td>436</td>\n",
       "      <td>64698</td>\n",
       "      <td>21106</td>\n",
       "      <td>1492</td>\n",
       "      <td>62679</td>\n",
       "      <td>253</td>\n",
       "      <td>396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_HSCs-Ctrl3</th>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>...</td>\n",
       "      <td>155365</td>\n",
       "      <td>377</td>\n",
       "      <td>438</td>\n",
       "      <td>480</td>\n",
       "      <td>85650</td>\n",
       "      <td>31860</td>\n",
       "      <td>2033</td>\n",
       "      <td>89559</td>\n",
       "      <td>282</td>\n",
       "      <td>448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_HSCs-TGFb1</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>...</td>\n",
       "      <td>110866</td>\n",
       "      <td>373</td>\n",
       "      <td>441</td>\n",
       "      <td>481</td>\n",
       "      <td>60325</td>\n",
       "      <td>19496</td>\n",
       "      <td>1447</td>\n",
       "      <td>66283</td>\n",
       "      <td>172</td>\n",
       "      <td>341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32_HSCs-TGFb2</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>...</td>\n",
       "      <td>45488</td>\n",
       "      <td>239</td>\n",
       "      <td>331</td>\n",
       "      <td>343</td>\n",
       "      <td>27442</td>\n",
       "      <td>9054</td>\n",
       "      <td>624</td>\n",
       "      <td>27535</td>\n",
       "      <td>96</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33_HSCs-TGFb3</th>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>497</td>\n",
       "      <td>45443</td>\n",
       "      <td>13796</td>\n",
       "      <td>1077</td>\n",
       "      <td>43415</td>\n",
       "      <td>192</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6 rows × 64253 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GeneName       DDX11L1  WASH7P  MIR6859-1  MIR1302-11  MIR1302-9  FAM138A  \\\n",
       "25_HSCs-Ctrl1        0       9         10           1          0        0   \n",
       "26_HSCs-Ctrl2        0      12         14           0          0        0   \n",
       "27_HSCs-Ctrl3        0      14         10           0          0        0   \n",
       "31_HSCs-TGFb1        0      11         16           0          0        0   \n",
       "32_HSCs-TGFb2        0       5          8           0          0        0   \n",
       "33_HSCs-TGFb3        0      12          5           0          0        0   \n",
       "\n",
       "GeneName       OR4G4P  OR4G11P  OR4F5  RP11-34P13.7  ...  MT-ND4  MT-TH  \\\n",
       "25_HSCs-Ctrl1       0        0      0            33  ...   93192    342   \n",
       "26_HSCs-Ctrl2       0        0      0            66  ...  114914    355   \n",
       "27_HSCs-Ctrl3       0        0      0            52  ...  155365    377   \n",
       "31_HSCs-TGFb1       0        0      0            54  ...  110866    373   \n",
       "32_HSCs-TGFb2       0        0      0            44  ...   45488    239   \n",
       "33_HSCs-TGFb3       0        0      0            32  ...   70704    344   \n",
       "\n",
       "GeneName       MT-TS2  MT-TL2  MT-ND5  MT-ND6  MT-TE  MT-CYB  MT-TT  MT-TP  \n",
       "25_HSCs-Ctrl1     476     493   54466   17184   1302   54099    258    475  \n",
       "26_HSCs-Ctrl2     388     436   64698   21106   1492   62679    253    396  \n",
       "27_HSCs-Ctrl3     438     480   85650   31860   2033   89559    282    448  \n",
       "31_HSCs-TGFb1     441     481   60325   19496   1447   66283    172    341  \n",
       "32_HSCs-TGFb2     331     343   27442    9054    624   27535     96    216  \n",
       "33_HSCs-TGFb3     453     497   45443   13796   1077   43415    192    243  \n",
       "\n",
       "[6 rows x 64253 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read raw data and process it\n",
    "adata = pd.read_csv('counts.txt', index_col=2, sep='\\t').iloc[:, 5:].T\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "212f7c7c",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "In case your data does not contain gene symbols but rather gene ids like ENSMBL, you can use the function `sc.queries.biomart_annotations` to retrieve them.\n",
    "In this other [vignette](https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html), ENSMBL ids are transformed into gene symbols.\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6335a2fe",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "In case your data is not from human but rather a model organism, you can find their human orthologs using `pypath`.\n",
    "Check this GitHub [issue](https://github.com/saezlab/decoupler-py/issues/5#issuecomment-1137099265) where mouse genes are transformed into human.\n",
    "\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bf58d9c",
   "metadata": {},
   "source": [
    "The obtained data consist of raw read counts for six different samples (three controls, three treatments) for ~60k genes.\n",
    "Before continuing, we will transform our expression data into an `AnnData` object. It handles annotated data matrices\n",
    "efficiently in memory and on disk and is used in the scverse framework. You can read more about it \n",
    "[here](https://scverse.org/) and [here](https://anndata.readthedocs.io/en/latest/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6d5ab0b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/badi/miniconda3/envs/dcp39/lib/python3.9/site-packages/anndata/_core/anndata.py:1830: UserWarning: Variable names are not unique. To make them unique, call `.var_names_make_unique`.\n",
      "  utils.warn_names_duplicates(\"var\")\n",
      "/home/badi/miniconda3/envs/dcp39/lib/python3.9/site-packages/anndata/utils.py:111: UserWarning: Suffix used (-[0-9]+) to deduplicate index values may make index values difficult to interpret. There values with a similar suffixes in the index. Consider using a different delimiter by passing `join={delimiter}`Example key collisions generated by the make_index_unique algorithm: ['SNORD116-1', 'SNORD116-2', 'SNORD116-3', 'SNORD116-4', 'SNORD116-5']\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6 × 64253"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transform to AnnData object\n",
    "adata = AnnData(adata, dtype=np.float32)\n",
    "adata.var_names_make_unique()\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42c4244a",
   "metadata": {},
   "source": [
    "Inside an `AnnData` object, there is the `.obs` attribute where we can store the metadata of our samples.\n",
    "Here we will infer the metadata by processing the sample ids, but this could also be added from a separate dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "836b0d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>condition</th>\n",
       "      <th>sample_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25_HSCs-Ctrl1</th>\n",
       "      <td>control</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26_HSCs-Ctrl2</th>\n",
       "      <td>control</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27_HSCs-Ctrl3</th>\n",
       "      <td>control</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31_HSCs-TGFb1</th>\n",
       "      <td>treatment</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32_HSCs-TGFb2</th>\n",
       "      <td>treatment</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33_HSCs-TGFb3</th>\n",
       "      <td>treatment</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               condition sample_id\n",
       "25_HSCs-Ctrl1    control        25\n",
       "26_HSCs-Ctrl2    control        26\n",
       "27_HSCs-Ctrl3    control        27\n",
       "31_HSCs-TGFb1  treatment        31\n",
       "32_HSCs-TGFb2  treatment        32\n",
       "33_HSCs-TGFb3  treatment        33"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Process treatment information\n",
    "adata.obs['condition'] = ['control' if '-Ctrl' in sample_id else 'treatment' for sample_id in adata.obs.index]\n",
    "\n",
    "# Process sample information\n",
    "adata.obs['sample_id'] = [sample_id.split('_')[0] for sample_id in adata.obs.index]\n",
    "\n",
    "# Visualize metadata\n",
    "adata.obs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc5f2a9b",
   "metadata": {},
   "source": [
    "## Quality control\n",
    "\n",
    "Before doing anything we need to ensure that our data passes some quality control thresholds. In transcriptomics it can happen\n",
    "that some genes were not properly profiled and thus need to be removed.\n",
    "\n",
    "To filter genes, we will follow the strategy implemented in the function `filterByExpr` from [edgeR](https://rdrr.io/bioc/edgeR/man/filterByExpr.html).\n",
    "It keeps genes that have a minimum total number of reads across samples (`min_total_count`), and that have a minimum number of counts in a number of samples (`min_count`).\n",
    "\n",
    "We can plot how many genes do we keep, you can play with the `min_count` and `min_total_count` to check how many genes would be kept when changed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a40896c-568e-4a86-a1b7-a5d1b2cb9e3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_filter_by_expr(adata, group=None, min_count=10, min_total_count=15, large_n=1, min_prop=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90e9b2ae-403e-4bfe-9471-e16ca3477744",
   "metadata": {},
   "source": [
    "Here we can observe the frequency of genes with different total sum of counts and number of samples. The dashed lines indicate the current thresholds, meaning that only the genes in the upper-right corner are going to be kept. Filtering parameters is completely arbitrary, but a good rule of thumb is to identify bimodal distributions and split them modifying the available thresholds.\n",
    "In this example, with the default values we would keep a good quantity of genes while filtering potential noisy genes.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "Changing the value of `min_count` will drastically change the distribution of \"Number of samples\", not change its threshold.\n",
    "In case you want to lower or increase it, you need to play with the `group`, `large_n` and `min_prop` parameters. \n",
    "\n",
    "\n",
    "</div>\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "\n",
    "This thresholds can vary a lot between datasets, manual assessment of them needs to be considered. For example, it might be\n",
    "the case that many genes are not expressed in just one sample which they would get removed by the current setting. For this\n",
    "specific dataset it is fine.\n",
    "\n",
    "</div>\n",
    "\n",
    "Once we are content with the threshold parameters, we can perform the actual filtering:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6c681d17-998f-4319-8eec-b067dbe6a016",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 6 × 17575\n",
       "    obs: 'condition', 'sample_id'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtain genes that pass the thresholds\n",
    "genes = dc.filter_by_expr(adata, group=None, min_count=10, min_total_count=15, large_n=1, min_prop=1)\n",
    "\n",
    "# Filter by these genes\n",
    "adata = adata[:, genes].copy()\n",
    "adata"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fa8b2a1-7c77-4d66-afbd-cb0559157857",
   "metadata": {},
   "source": [
    "## Differential expression analysis\n",
    "\n",
    "In order to identify which are the genes that are changing the most between treatment and control we can perform differential\n",
    "expression analysis (DEA). For this example, we will perform a simple experimental design where we compare the gene expression\n",
    "of treated cells against controls. We will use the python implementation of the framework DESeq2, but we could have used any\n",
    "other one (`limma` or `edgeR` for example). For a better understanding how it works, check\n",
    "[DESeq2's documentation](https://pydeseq2.readthedocs.io/en/latest/). Note that more complex experimental designs can be used\n",
    "by adding more factors to the `design_factors` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4df3c8ca-e94a-44d9-8ff7-2c51f13e56b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import DESeq2\n",
    "from pydeseq2.dds import DeseqDataSet\n",
    "from pydeseq2.ds import DeseqStats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b6e6ca8a-97c6-4cfe-8662-1833911b5b64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build DESeq2 object\n",
    "dds = DeseqDataSet(\n",
    "    adata=adata,\n",
    "    design_factors='condition',\n",
    "    refit_cooks=True,\n",
    "    n_cpus=8,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "96cc03ee-f9c2-4e47-9660-642ceecf6e6f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting size factors...\n",
      "... done in 0.01 seconds.\n",
      "\n",
      "Fitting dispersions...\n",
      "... done in 39.97 seconds.\n",
      "\n",
      "Fitting dispersion trend curve...\n",
      "... done in 7.35 seconds.\n",
      "\n",
      "Fitting MAP dispersions...\n",
      "... done in 43.56 seconds.\n",
      "\n",
      "Fitting LFCs...\n",
      "... done in 3.16 seconds.\n",
      "\n",
      "Refitting 0 outliers.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Compute LFCs\n",
    "dds.deseq2()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c28301e1-a2ad-411a-a8ab-08e7d65fd43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract contrast between COVID-19 vs normal\n",
    "stat_res = DeseqStats(dds, contrast=[\"condition\", 'treatment', 'control'], n_cpus=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44a071fb-41ed-4dc4-ab97-89cd11ddc4f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Wald tests...\n",
      "... done in 1.53 seconds.\n",
      "\n",
      "Log2 fold change & Wald test p-value: condition treatment vs control\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
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       "      <td>0.819639</td>\n",
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       "      <td>0.249617</td>\n",
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       "      <td>0.729765</td>\n",
       "      <td>0.801351</td>\n",
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       "      <th>CICP27</th>\n",
       "      <td>106.257057</td>\n",
       "      <td>0.144798</td>\n",
       "      <td>0.177248</td>\n",
       "      <td>0.816925</td>\n",
       "      <td>0.413971</td>\n",
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       "      <td>0.286075</td>\n",
       "      <td>-2.217747</td>\n",
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       "      <td>0.579098</td>\n",
       "      <td>0.270626</td>\n",
       "      <td>2.139848</td>\n",
       "      <td>0.032367</td>\n",
       "      <td>0.060587</td>\n",
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       "      <td>17962.507812</td>\n",
       "      <td>-0.445816</td>\n",
       "      <td>0.278378</td>\n",
       "      <td>-1.601475</td>\n",
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       "      <td>1284.633179</td>\n",
       "      <td>-0.343227</td>\n",
       "      <td>0.287943</td>\n",
       "      <td>-1.191996</td>\n",
       "      <td>0.233263</td>\n",
       "      <td>0.330373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-CYB</th>\n",
       "      <td>55097.121094</td>\n",
       "      <td>-0.323882</td>\n",
       "      <td>0.302438</td>\n",
       "      <td>-1.070903</td>\n",
       "      <td>0.284213</td>\n",
       "      <td>0.387995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TT</th>\n",
       "      <td>205.288528</td>\n",
       "      <td>-0.495090</td>\n",
       "      <td>0.332233</td>\n",
       "      <td>-1.490190</td>\n",
       "      <td>0.136174</td>\n",
       "      <td>0.210545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TP</th>\n",
       "      <td>346.101318</td>\n",
       "      <td>-0.473558</td>\n",
       "      <td>0.200422</td>\n",
       "      <td>-2.362806</td>\n",
       "      <td>0.018137</td>\n",
       "      <td>0.036124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17575 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "GeneName                                                                   \n",
       "RP11-34P13.7     45.820190        0.067094  0.294257  0.228010  0.819639   \n",
       "RP11-34P13.8     29.560568       -0.086227  0.249617 -0.345438  0.729765   \n",
       "CICP27          106.257057        0.144798  0.177248  0.816925  0.413971   \n",
       "FO538757.2       33.114796       -0.634443  0.286075 -2.217747  0.026572   \n",
       "AP006222.2       67.154701        0.579098  0.270626  2.139848  0.032367   \n",
       "...                    ...             ...       ...       ...       ...   \n",
       "MT-ND6        17962.507812       -0.445816  0.278378 -1.601475  0.109272   \n",
       "MT-TE          1284.633179       -0.343227  0.287943 -1.191996  0.233263   \n",
       "MT-CYB        55097.121094       -0.323882  0.302438 -1.070903  0.284213   \n",
       "MT-TT           205.288528       -0.495090  0.332233 -1.490190  0.136174   \n",
       "MT-TP           346.101318       -0.473558  0.200422 -2.362806  0.018137   \n",
       "\n",
       "                  padj  \n",
       "GeneName                \n",
       "RP11-34P13.7  0.871654  \n",
       "RP11-34P13.8  0.801351  \n",
       "CICP27        0.521994  \n",
       "FO538757.2    0.050794  \n",
       "AP006222.2    0.060587  \n",
       "...                ...  \n",
       "MT-ND6        0.175000  \n",
       "MT-TE         0.330373  \n",
       "MT-CYB        0.387995  \n",
       "MT-TT         0.210545  \n",
       "MT-TP         0.036124  \n",
       "\n",
       "[17575 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute Wald test\n",
    "stat_res.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2368658c-70c1-4194-917c-dc72f59a0a3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting MAP LFCs...\n",
      "... done in 6.70 seconds.\n",
      "\n",
      "Shrunk Log2 fold change & Wald test p-value: condition treatment vs control\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeneName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.7</th>\n",
       "      <td>45.820190</td>\n",
       "      <td>0.050407</td>\n",
       "      <td>0.271447</td>\n",
       "      <td>0.228010</td>\n",
       "      <td>0.819639</td>\n",
       "      <td>0.871654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.8</th>\n",
       "      <td>29.560568</td>\n",
       "      <td>-0.068949</td>\n",
       "      <td>0.234608</td>\n",
       "      <td>-0.345438</td>\n",
       "      <td>0.729765</td>\n",
       "      <td>0.801351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CICP27</th>\n",
       "      <td>106.257057</td>\n",
       "      <td>0.129368</td>\n",
       "      <td>0.172303</td>\n",
       "      <td>0.816925</td>\n",
       "      <td>0.413971</td>\n",
       "      <td>0.521994</td>\n",
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       "    <tr>\n",
       "      <th>FO538757.2</th>\n",
       "      <td>33.114796</td>\n",
       "      <td>-0.523892</td>\n",
       "      <td>0.280505</td>\n",
       "      <td>-2.217747</td>\n",
       "      <td>0.026572</td>\n",
       "      <td>0.050794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP006222.2</th>\n",
       "      <td>67.154701</td>\n",
       "      <td>0.483006</td>\n",
       "      <td>0.266258</td>\n",
       "      <td>2.139848</td>\n",
       "      <td>0.032367</td>\n",
       "      <td>0.060587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND6</th>\n",
       "      <td>17962.507812</td>\n",
       "      <td>-0.240886</td>\n",
       "      <td>0.260519</td>\n",
       "      <td>-1.601475</td>\n",
       "      <td>0.109272</td>\n",
       "      <td>0.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TE</th>\n",
       "      <td>1284.633179</td>\n",
       "      <td>-0.265368</td>\n",
       "      <td>0.273098</td>\n",
       "      <td>-1.191996</td>\n",
       "      <td>0.233263</td>\n",
       "      <td>0.330373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-CYB</th>\n",
       "      <td>55097.121094</td>\n",
       "      <td>-0.523154</td>\n",
       "      <td>0.297741</td>\n",
       "      <td>-1.070903</td>\n",
       "      <td>0.284213</td>\n",
       "      <td>0.387995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TT</th>\n",
       "      <td>205.288528</td>\n",
       "      <td>-0.366886</td>\n",
       "      <td>0.316293</td>\n",
       "      <td>-1.490190</td>\n",
       "      <td>0.136174</td>\n",
       "      <td>0.210545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TP</th>\n",
       "      <td>346.101318</td>\n",
       "      <td>-0.424278</td>\n",
       "      <td>0.197644</td>\n",
       "      <td>-2.362806</td>\n",
       "      <td>0.018137</td>\n",
       "      <td>0.036124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17575 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "GeneName                                                                   \n",
       "RP11-34P13.7     45.820190        0.050407  0.271447  0.228010  0.819639   \n",
       "RP11-34P13.8     29.560568       -0.068949  0.234608 -0.345438  0.729765   \n",
       "CICP27          106.257057        0.129368  0.172303  0.816925  0.413971   \n",
       "FO538757.2       33.114796       -0.523892  0.280505 -2.217747  0.026572   \n",
       "AP006222.2       67.154701        0.483006  0.266258  2.139848  0.032367   \n",
       "...                    ...             ...       ...       ...       ...   \n",
       "MT-ND6        17962.507812       -0.240886  0.260519 -1.601475  0.109272   \n",
       "MT-TE          1284.633179       -0.265368  0.273098 -1.191996  0.233263   \n",
       "MT-CYB        55097.121094       -0.523154  0.297741 -1.070903  0.284213   \n",
       "MT-TT           205.288528       -0.366886  0.316293 -1.490190  0.136174   \n",
       "MT-TP           346.101318       -0.424278  0.197644 -2.362806  0.018137   \n",
       "\n",
       "                  padj  \n",
       "GeneName                \n",
       "RP11-34P13.7  0.871654  \n",
       "RP11-34P13.8  0.801351  \n",
       "CICP27        0.521994  \n",
       "FO538757.2    0.050794  \n",
       "AP006222.2    0.060587  \n",
       "...                ...  \n",
       "MT-ND6        0.175000  \n",
       "MT-TE         0.330373  \n",
       "MT-CYB        0.387995  \n",
       "MT-TT         0.210545  \n",
       "MT-TP         0.036124  \n",
       "\n",
       "[17575 rows x 6 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shrink LFCs\n",
    "stat_res.lfc_shrink(coeff='condition_treatment_vs_control')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "01376338-4f96-4a3b-8d5f-deb06054e7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseMean</th>\n",
       "      <th>log2FoldChange</th>\n",
       "      <th>lfcSE</th>\n",
       "      <th>stat</th>\n",
       "      <th>pvalue</th>\n",
       "      <th>padj</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GeneName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.7</th>\n",
       "      <td>45.820190</td>\n",
       "      <td>0.050407</td>\n",
       "      <td>0.271447</td>\n",
       "      <td>0.228010</td>\n",
       "      <td>0.819639</td>\n",
       "      <td>0.871654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RP11-34P13.8</th>\n",
       "      <td>29.560568</td>\n",
       "      <td>-0.068949</td>\n",
       "      <td>0.234608</td>\n",
       "      <td>-0.345438</td>\n",
       "      <td>0.729765</td>\n",
       "      <td>0.801351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CICP27</th>\n",
       "      <td>106.257057</td>\n",
       "      <td>0.129368</td>\n",
       "      <td>0.172303</td>\n",
       "      <td>0.816925</td>\n",
       "      <td>0.413971</td>\n",
       "      <td>0.521994</td>\n",
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       "    <tr>\n",
       "      <th>FO538757.2</th>\n",
       "      <td>33.114796</td>\n",
       "      <td>-0.523892</td>\n",
       "      <td>0.280505</td>\n",
       "      <td>-2.217747</td>\n",
       "      <td>0.026572</td>\n",
       "      <td>0.050794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AP006222.2</th>\n",
       "      <td>67.154701</td>\n",
       "      <td>0.483006</td>\n",
       "      <td>0.266258</td>\n",
       "      <td>2.139848</td>\n",
       "      <td>0.032367</td>\n",
       "      <td>0.060587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-ND6</th>\n",
       "      <td>17962.507812</td>\n",
       "      <td>-0.240886</td>\n",
       "      <td>0.260519</td>\n",
       "      <td>-1.601475</td>\n",
       "      <td>0.109272</td>\n",
       "      <td>0.175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TE</th>\n",
       "      <td>1284.633179</td>\n",
       "      <td>-0.265368</td>\n",
       "      <td>0.273098</td>\n",
       "      <td>-1.191996</td>\n",
       "      <td>0.233263</td>\n",
       "      <td>0.330373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-CYB</th>\n",
       "      <td>55097.121094</td>\n",
       "      <td>-0.523154</td>\n",
       "      <td>0.297741</td>\n",
       "      <td>-1.070903</td>\n",
       "      <td>0.284213</td>\n",
       "      <td>0.387995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TT</th>\n",
       "      <td>205.288528</td>\n",
       "      <td>-0.366886</td>\n",
       "      <td>0.316293</td>\n",
       "      <td>-1.490190</td>\n",
       "      <td>0.136174</td>\n",
       "      <td>0.210545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MT-TP</th>\n",
       "      <td>346.101318</td>\n",
       "      <td>-0.424278</td>\n",
       "      <td>0.197644</td>\n",
       "      <td>-2.362806</td>\n",
       "      <td>0.018137</td>\n",
       "      <td>0.036124</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>17575 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
       "GeneName                                                                   \n",
       "RP11-34P13.7     45.820190        0.050407  0.271447  0.228010  0.819639   \n",
       "RP11-34P13.8     29.560568       -0.068949  0.234608 -0.345438  0.729765   \n",
       "CICP27          106.257057        0.129368  0.172303  0.816925  0.413971   \n",
       "FO538757.2       33.114796       -0.523892  0.280505 -2.217747  0.026572   \n",
       "AP006222.2       67.154701        0.483006  0.266258  2.139848  0.032367   \n",
       "...                    ...             ...       ...       ...       ...   \n",
       "MT-ND6        17962.507812       -0.240886  0.260519 -1.601475  0.109272   \n",
       "MT-TE          1284.633179       -0.265368  0.273098 -1.191996  0.233263   \n",
       "MT-CYB        55097.121094       -0.523154  0.297741 -1.070903  0.284213   \n",
       "MT-TT           205.288528       -0.366886  0.316293 -1.490190  0.136174   \n",
       "MT-TP           346.101318       -0.424278  0.197644 -2.362806  0.018137   \n",
       "\n",
       "                  padj  \n",
       "GeneName                \n",
       "RP11-34P13.7  0.871654  \n",
       "RP11-34P13.8  0.801351  \n",
       "CICP27        0.521994  \n",
       "FO538757.2    0.050794  \n",
       "AP006222.2    0.060587  \n",
       "...                ...  \n",
       "MT-ND6        0.175000  \n",
       "MT-TE         0.330373  \n",
       "MT-CYB        0.387995  \n",
       "MT-TT         0.210545  \n",
       "MT-TP         0.036124  \n",
       "\n",
       "[17575 rows x 6 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extract results\n",
    "results_df = stat_res.results_df\n",
    "results_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9e0f1e2-c8a4-4d2e-afaf-d454d4e5b40e",
   "metadata": {},
   "source": [
    "We can plot the obtained results in a volcano plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f0ceebe8-97ad-4447-aef2-8bd63e9f414e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/badi/miniconda3/envs/dcp39/lib/python3.9/site-packages/pandas/core/arraylike.py:402: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = getattr(ufunc, method)(*inputs, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_volcano_df(results_df, x='log2FoldChange', y='padj', top=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8676b266-dd45-495f-baee-c1c684a5d445",
   "metadata": {},
   "source": [
    "After performing DEA, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used,\n",
    "but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value.\n",
    "We will transform the obtained t-values stored in `stats` to a wide matrix so that it can be used by `decoupler`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "715f76e6-a2a5-46bc-9c11-e4f352c651fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GeneName</th>\n",
       "      <th>RP11-34P13.7</th>\n",
       "      <th>RP11-34P13.8</th>\n",
       "      <th>CICP27</th>\n",
       "      <th>FO538757.2</th>\n",
       "      <th>AP006222.2</th>\n",
       "      <th>RP4-669L17.10</th>\n",
       "      <th>MTND1P23</th>\n",
       "      <th>MTND2P28</th>\n",
       "      <th>AC114498.1</th>\n",
       "      <th>MIR6723</th>\n",
       "      <th>...</th>\n",
       "      <th>MT-ND4</th>\n",
       "      <th>MT-TH</th>\n",
       "      <th>MT-TS2</th>\n",
       "      <th>MT-TL2</th>\n",
       "      <th>MT-ND5</th>\n",
       "      <th>MT-ND6</th>\n",
       "      <th>MT-TE</th>\n",
       "      <th>MT-CYB</th>\n",
       "      <th>MT-TT</th>\n",
       "      <th>MT-TP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>treatment.vs.control</th>\n",
       "      <td>0.22801</td>\n",
       "      <td>-0.345438</td>\n",
       "      <td>0.816925</td>\n",
       "      <td>-2.217747</td>\n",
       "      <td>2.139848</td>\n",
       "      <td>-0.422515</td>\n",
       "      <td>-1.373661</td>\n",
       "      <td>-0.53866</td>\n",
       "      <td>-2.845663</td>\n",
       "      <td>-3.862065</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.472827</td>\n",
       "      <td>0.550156</td>\n",
       "      <td>0.704443</td>\n",
       "      <td>1.049607</td>\n",
       "      <td>-1.283784</td>\n",
       "      <td>-1.601475</td>\n",
       "      <td>-1.191996</td>\n",
       "      <td>-1.070903</td>\n",
       "      <td>-1.49019</td>\n",
       "      <td>-2.362806</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 17575 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "GeneName              RP11-34P13.7  RP11-34P13.8    CICP27  FO538757.2  \\\n",
       "treatment.vs.control       0.22801     -0.345438  0.816925   -2.217747   \n",
       "\n",
       "GeneName              AP006222.2  RP4-669L17.10  MTND1P23  MTND2P28  \\\n",
       "treatment.vs.control    2.139848      -0.422515 -1.373661  -0.53866   \n",
       "\n",
       "GeneName              AC114498.1   MIR6723  ...    MT-ND4     MT-TH    MT-TS2  \\\n",
       "treatment.vs.control   -2.845663 -3.862065  ... -1.472827  0.550156  0.704443   \n",
       "\n",
       "GeneName                MT-TL2    MT-ND5    MT-ND6     MT-TE    MT-CYB  \\\n",
       "treatment.vs.control  1.049607 -1.283784 -1.601475 -1.191996 -1.070903   \n",
       "\n",
       "GeneName                MT-TT     MT-TP  \n",
       "treatment.vs.control -1.49019 -2.362806  \n",
       "\n",
       "[1 rows x 17575 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = results_df[['stat']].T.rename(index={'stat': 'treatment.vs.control'})\n",
    "mat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fe9dee7",
   "metadata": {},
   "source": [
    "## Transcription factor activity inference\n",
    "\n",
    "The first functional analysis we can perform is to infer transcription factor (TF) activities from our transcriptomics data. We will need a gene regulatory network (GRN) and a statistical method.\n",
    "\n",
    "### CollecTRI network\n",
    "[CollecTRI](https://github.com/saezlab/CollecTRI) is a comprehensive resource\n",
    "containing a curated collection of TFs and their transcriptional targets\n",
    "compiled from 12 different resources. This collection provides an increased\n",
    "coverage of transcription factors and a superior performance in identifying\n",
    "perturbed TFs compared to our previous\n",
    "[DoRothEA](https://saezlab.github.io/dorothea/) network and other literature\n",
    "based GRNs. Similar to DoRothEA, interactions are weighted by their mode of\n",
    "regulation (activation or inhibition).\n",
    "\n",
    "For this example we will use the human version (mouse and rat are also\n",
    "available). We can use `decoupler` to retrieve it from `omnipath`. The argument\n",
    "`split_complexes` keeps complexes or splits them into subunits, by default we\n",
    "recommend to keep complexes together.\n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "\n",
    "In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as [CellOracle](https://morris-lab.github.io/CellOracle.documentation/), [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) or [SCENIC+](https://scenicplus.readthedocs.io/en/latest/). \n",
    "\n",
    "</div> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b8386a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MYC</td>\n",
       "      <td>TERT</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPI1</td>\n",
       "      <td>BGLAP</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SMAD3</td>\n",
       "      <td>JUN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SMAD4</td>\n",
       "      <td>JUN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>STAT5A</td>\n",
       "      <td>IL2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43173</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-143-3p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43174</th>\n",
       "      <td>AP1</td>\n",
       "      <td>hsa-miR-206</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43175</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-21-5p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43176</th>\n",
       "      <td>NFKB</td>\n",
       "      <td>hsa-miR-224-5p</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43177</th>\n",
       "      <td>AP1</td>\n",
       "      <td>hsa-miR-144</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>43178 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       source          target  weight\n",
       "0         MYC            TERT       1\n",
       "1        SPI1           BGLAP       1\n",
       "2       SMAD3             JUN       1\n",
       "3       SMAD4             JUN       1\n",
       "4      STAT5A             IL2       1\n",
       "...       ...             ...     ...\n",
       "43173    NFKB  hsa-miR-143-3p       1\n",
       "43174     AP1     hsa-miR-206       1\n",
       "43175    NFKB   hsa-miR-21-5p       1\n",
       "43176    NFKB  hsa-miR-224-5p       1\n",
       "43177     AP1     hsa-miR-144       1\n",
       "\n",
       "[43178 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve CollecTRI gene regulatory network\n",
    "collectri = dc.get_collectri(organism='human', split_complexes=False)\n",
    "collectri"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bfb8c1d-3709-471a-804f-71a261bb9f97",
   "metadata": {},
   "source": [
    "### Activity inference with Univariate Linear Model (ULM)\n",
    "\n",
    "To infer TF enrichment scores we will run the Univariate Linear Model (`ulm`) method. For each sample in our dataset (`mat`) and each TF in our network (`net`), it fits a linear model that predicts the observed gene expression\n",
    "based solely on the TF's TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.\n",
    "\n",
    "<img src=\"../ulm.png\" />\n",
    "\n",
    "We can run `ulm` with a one-liner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e46d54de-e219-4d5d-8ccf-f3eb3619c204",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ulm on mat with 1 samples and 17575 targets for 629 sources.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ABL1</th>\n",
       "      <th>AHR</th>\n",
       "      <th>AIRE</th>\n",
       "      <th>AP1</th>\n",
       "      <th>APEX1</th>\n",
       "      <th>AR</th>\n",
       "      <th>ARID1A</th>\n",
       "      <th>ARID3A</th>\n",
       "      <th>ARID3B</th>\n",
       "      <th>ARID4A</th>\n",
       "      <th>...</th>\n",
       "      <th>ZNF354C</th>\n",
       "      <th>ZNF362</th>\n",
       "      <th>ZNF382</th>\n",
       "      <th>ZNF384</th>\n",
       "      <th>ZNF395</th>\n",
       "      <th>ZNF436</th>\n",
       "      <th>ZNF699</th>\n",
       "      <th>ZNF76</th>\n",
       "      <th>ZNF804A</th>\n",
       "      <th>ZNF91</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>treatment.vs.control</th>\n",
       "      <td>-1.757384</td>\n",
       "      <td>-2.041384</td>\n",
       "      <td>-1.317279</td>\n",
       "      <td>-2.718626</td>\n",
       "      <td>-2.338566</td>\n",
       "      <td>0.178862</td>\n",
       "      <td>-6.440889</td>\n",
       "      <td>1.410645</td>\n",
       "      <td>2.23343</td>\n",
       "      <td>-0.704304</td>\n",
       "      <td>...</td>\n",
       "      <td>1.164067</td>\n",
       "      <td>-0.933561</td>\n",
       "      <td>2.687689</td>\n",
       "      <td>1.49982</td>\n",
       "      <td>-1.249087</td>\n",
       "      <td>1.407577</td>\n",
       "      <td>0.820753</td>\n",
       "      <td>0.485726</td>\n",
       "      <td>0.782225</td>\n",
       "      <td>0.86518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 629 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ABL1       AHR      AIRE       AP1     APEX1  \\\n",
       "treatment.vs.control -1.757384 -2.041384 -1.317279 -2.718626 -2.338566   \n",
       "\n",
       "                            AR    ARID1A    ARID3A   ARID3B    ARID4A  ...  \\\n",
       "treatment.vs.control  0.178862 -6.440889  1.410645  2.23343 -0.704304  ...   \n",
       "\n",
       "                       ZNF354C    ZNF362    ZNF382   ZNF384    ZNF395  \\\n",
       "treatment.vs.control  1.164067 -0.933561  2.687689  1.49982 -1.249087   \n",
       "\n",
       "                        ZNF436    ZNF699     ZNF76   ZNF804A    ZNF91  \n",
       "treatment.vs.control  1.407577  0.820753  0.485726  0.782225  0.86518  \n",
       "\n",
       "[1 rows x 629 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infer TF activities with ulm\n",
    "tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri, verbose=True)\n",
    "tf_acts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3834731",
   "metadata": {},
   "source": [
    "Let us plot the obtained scores for the top active/inactive transcription factors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f810e11d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_barplot(tf_acts, 'treatment.vs.control', top=25, vertical=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4cf5b1",
   "metadata": {},
   "source": [
    "SRF, MYOCD and SMAD3 seem to be the most activated in this treatment while IRF1, KLF11 AND ARID1A seem to be inactivated.\n",
    "\n",
    "As before, we can manually inspect the downstream targets of each transcription factor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f3256b33-2797-4f3d-8cbe-67f950cedf29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_targets(results_df, stat='stat', source_name='SRF', net=collectri, top=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da98dd02-84ea-4b29-9231-bbf40378defa",
   "metadata": {},
   "source": [
    "SRF seems to be active in treated cells since their positive targets are up-regulated.\n",
    "\n",
    "If needed, we can also look at this at the volcano plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "92d55a88",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/badi/miniconda3/envs/dcp39/lib/python3.9/site-packages/pandas/core/internals/blocks.py:351: RuntimeWarning: divide by zero encountered in log10\n",
      "  result = func(self.values, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract logFCs and pvals\n",
    "logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'treatment.vs.control'})\n",
    "pvals = results_df[['padj']].T.rename(index={'padj': 'treatment.vs.control'})\n",
    "\n",
    "# Plot\n",
    "dc.plot_volcano(logFCs, pvals, 'treatment.vs.control', name='SRF', net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9efaec0f-ac00-49a4-8297-53a310ec1bb3",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Pathway activity inference\n",
    "\n",
    "Another analysis we can perform is to infer pathway activities from our transcriptomics data.\n",
    "\n",
    "### PROGENy model\n",
    "\n",
    "[PROGENy](https://saezlab.github.io/progeny/) is a comprehensive resource containing a curated collection of pathways and their target genes, with weights for each interaction.\n",
    "For this example we will use the human weights (other organisms are available) and we will use the top 500 responsive genes ranked by p-value. Here is a brief description of each pathway:\n",
    "\n",
    "- **Androgen**: involved in the growth and development of the male reproductive organs.\n",
    "- **EGFR**: regulates growth, survival, migration, apoptosis, proliferation, and differentiation in mammalian cells\n",
    "- **Estrogen**: promotes the growth and development of the female reproductive organs.\n",
    "- **Hypoxia**: promotes angiogenesis and metabolic reprogramming when O2 levels are low.\n",
    "- **JAK-STAT**: involved in immunity, cell division, cell death, and tumor formation.\n",
    "- **MAPK**: integrates external signals and promotes cell growth and proliferation.\n",
    "- **NFkB**: regulates immune response, cytokine production and cell survival.\n",
    "- **p53**: regulates cell cycle, apoptosis, DNA repair and tumor suppression.\n",
    "- **PI3K**: promotes growth and proliferation.\n",
    "- **TGFb**: involved in development, homeostasis, and repair of most tissues.\n",
    "- **TNFa**: mediates haematopoiesis, immune surveillance, tumour regression and protection from infection.\n",
    "- **Trail**: induces apoptosis.\n",
    "- **VEGF**: mediates angiogenesis, vascular permeability, and cell migration.\n",
    "- **WNT**: regulates organ morphogenesis during development and tissue repair.\n",
    "\n",
    "To access it we can use `decoupler`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "e646969e-c4f6-48a5-8d19-848746ac7140",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>TMPRSS2</td>\n",
       "      <td>11.490631</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>NKX3-1</td>\n",
       "      <td>10.622551</td>\n",
       "      <td>2.242078e-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>MBOAT2</td>\n",
       "      <td>10.472733</td>\n",
       "      <td>4.624285e-44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>KLK2</td>\n",
       "      <td>10.176186</td>\n",
       "      <td>1.944414e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Androgen</td>\n",
       "      <td>SARG</td>\n",
       "      <td>11.386852</td>\n",
       "      <td>2.790209e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6995</th>\n",
       "      <td>p53</td>\n",
       "      <td>ZMYM4</td>\n",
       "      <td>-2.325752</td>\n",
       "      <td>1.522388e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6996</th>\n",
       "      <td>p53</td>\n",
       "      <td>CFDP1</td>\n",
       "      <td>-1.628168</td>\n",
       "      <td>1.526045e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6997</th>\n",
       "      <td>p53</td>\n",
       "      <td>VPS37D</td>\n",
       "      <td>2.309503</td>\n",
       "      <td>1.537098e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6998</th>\n",
       "      <td>p53</td>\n",
       "      <td>TEDC1</td>\n",
       "      <td>-2.274823</td>\n",
       "      <td>1.547037e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6999</th>\n",
       "      <td>p53</td>\n",
       "      <td>CCDC138</td>\n",
       "      <td>-3.205113</td>\n",
       "      <td>1.568160e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        source   target     weight       p_value\n",
       "0     Androgen  TMPRSS2  11.490631  0.000000e+00\n",
       "1     Androgen   NKX3-1  10.622551  2.242078e-44\n",
       "2     Androgen   MBOAT2  10.472733  4.624285e-44\n",
       "3     Androgen     KLK2  10.176186  1.944414e-40\n",
       "4     Androgen     SARG  11.386852  2.790209e-40\n",
       "...        ...      ...        ...           ...\n",
       "6995       p53    ZMYM4  -2.325752  1.522388e-06\n",
       "6996       p53    CFDP1  -1.628168  1.526045e-06\n",
       "6997       p53   VPS37D   2.309503  1.537098e-06\n",
       "6998       p53    TEDC1  -2.274823  1.547037e-06\n",
       "6999       p53  CCDC138  -3.205113  1.568160e-06\n",
       "\n",
       "[7000 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieve PROGENy model weights\n",
    "progeny = dc.get_progeny(top=500)\n",
    "progeny"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94b757a3-e7b9-4c2a-9081-4a3b4a17ee3e",
   "metadata": {},
   "source": [
    "### Activity inference with Multivariate Linear Model (MLM)\n",
    "\n",
    "To infer pathway enrichment scores we will run the Multivariate Linear Model (`mlm`) method. For each sample in our dataset (`adata`), it fits a linear model that predicts the observed gene expression based on all pathways' Pathway-Gene interactions weights.\n",
    "Once fitted, the obtained t-values of the slopes are the scores. If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive.\n",
    "\n",
    "<img src=\"../mlm.png\" />\n",
    "     \n",
    "We can run `mlm` with a one-liner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bd82e675-cb82-4fbe-8b93-50ceb73b7373",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running mlm on mat with 1 samples and 17575 targets for 14 sources.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1/1 [00:00<00:00,  6.86it/s]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Androgen</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>Estrogen</th>\n",
       "      <th>Hypoxia</th>\n",
       "      <th>JAK-STAT</th>\n",
       "      <th>MAPK</th>\n",
       "      <th>NFkB</th>\n",
       "      <th>PI3K</th>\n",
       "      <th>TGFb</th>\n",
       "      <th>TNFa</th>\n",
       "      <th>Trail</th>\n",
       "      <th>VEGF</th>\n",
       "      <th>WNT</th>\n",
       "      <th>p53</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>treatment.vs.control</th>\n",
       "      <td>1.92976</td>\n",
       "      <td>-1.739555</td>\n",
       "      <td>1.506251</td>\n",
       "      <td>6.360106</td>\n",
       "      <td>-11.535424</td>\n",
       "      <td>7.474121</td>\n",
       "      <td>0.744243</td>\n",
       "      <td>7.730807</td>\n",
       "      <td>37.363144</td>\n",
       "      <td>-5.540495</td>\n",
       "      <td>-2.149369</td>\n",
       "      <td>3.378371</td>\n",
       "      <td>-0.239079</td>\n",
       "      <td>-5.815882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Androgen      EGFR  Estrogen   Hypoxia   JAK-STAT  \\\n",
       "treatment.vs.control   1.92976 -1.739555  1.506251  6.360106 -11.535424   \n",
       "\n",
       "                          MAPK      NFkB      PI3K       TGFb      TNFa  \\\n",
       "treatment.vs.control  7.474121  0.744243  7.730807  37.363144 -5.540495   \n",
       "\n",
       "                         Trail      VEGF       WNT       p53  \n",
       "treatment.vs.control -2.149369  3.378371 -0.239079 -5.815882  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infer pathway activities with mlm\n",
    "pathway_acts, pathway_pvals = dc.run_mlm(mat=mat, net=progeny, verbose=True)\n",
    "pathway_acts"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d8aa60-3cff-476e-8050-a911932b1059",
   "metadata": {},
   "source": [
    "Let us plot the obtained scores:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ea55464a-8ead-408c-b9ce-0d8284758bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_barplot(pathway_acts, 'treatment.vs.control', top=25, vertical=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c413af-aaca-42a7-9980-c7bd8a6cd81d",
   "metadata": {},
   "source": [
    "As expected, after treating cells with the cytokine TGFb we see an increase of activity for this pathway.\n",
    "\n",
    "On the other hand, it seems that this treatment has decreased the activity of other pathways like JAK-STAT or TNFa.\n",
    "\n",
    "We can visualize the targets of TFGb in a scatter plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "6a237ad3-bae4-4fa2-b265-51b938f90250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_targets(results_df, stat='stat', source_name='TGFb', net=progeny, top=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49c87ac5",
   "metadata": {},
   "source": [
    "The observed activation of TGFb is due to the fact that majority of its target genes with positive weights have positive\n",
    "t-values (1st quadrant), and the majority of the ones with negative weights have negative t-values (3d quadrant)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bf0d157",
   "metadata": {},
   "source": [
    "## Functional enrichment of biological terms\n",
    "\n",
    "Finally, we can also infer activities for general biological terms or processes.\n",
    "\n",
    "### MSigDB gene sets\n",
    "\n",
    "The Molecular Signatures Database ([MSigDB](http://www.gsea-msigdb.org/gsea/msigdb/)) is a resource containing a collection of gene sets annotated to different biological processes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e781e130-7674-40ad-bed9-80f44d8d100a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genesymbol</th>\n",
       "      <th>collection</th>\n",
       "      <th>geneset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>chemical_and_genetic_perturbations</td>\n",
       "      <td>BOYAULT_LIVER_CANCER_SUBCLASS_G56_DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>chemical_and_genetic_perturbations</td>\n",
       "      <td>ELVIDGE_HYPOXIA_UP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>chemical_and_genetic_perturbations</td>\n",
       "      <td>NUYTTEN_NIPP1_TARGETS_DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>immunesigdb</td>\n",
       "      <td>GSE17721_POLYIC_VS_GARDIQUIMOD_4H_BMDC_DN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>chemical_and_genetic_perturbations</td>\n",
       "      <td>SCHAEFFER_PROSTATE_DEVELOPMENT_12HR_UP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838543</th>\n",
       "      <td>PRAMEF22</td>\n",
       "      <td>go_biological_process</td>\n",
       "      <td>GOBP_POSITIVE_REGULATION_OF_CELL_POPULATION_PR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838544</th>\n",
       "      <td>PRAMEF22</td>\n",
       "      <td>go_biological_process</td>\n",
       "      <td>GOBP_APOPTOTIC_PROCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838545</th>\n",
       "      <td>PRAMEF22</td>\n",
       "      <td>go_biological_process</td>\n",
       "      <td>GOBP_REGULATION_OF_CELL_DEATH</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838546</th>\n",
       "      <td>PRAMEF22</td>\n",
       "      <td>go_biological_process</td>\n",
       "      <td>GOBP_NEGATIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3838547</th>\n",
       "      <td>PRAMEF22</td>\n",
       "      <td>go_biological_process</td>\n",
       "      <td>GOBP_NEGATIVE_REGULATION_OF_CELL_DEATH</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3838548 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        genesymbol                          collection  \\\n",
       "0             MAFF  chemical_and_genetic_perturbations   \n",
       "1             MAFF  chemical_and_genetic_perturbations   \n",
       "2             MAFF  chemical_and_genetic_perturbations   \n",
       "3             MAFF                         immunesigdb   \n",
       "4             MAFF  chemical_and_genetic_perturbations   \n",
       "...            ...                                 ...   \n",
       "3838543   PRAMEF22               go_biological_process   \n",
       "3838544   PRAMEF22               go_biological_process   \n",
       "3838545   PRAMEF22               go_biological_process   \n",
       "3838546   PRAMEF22               go_biological_process   \n",
       "3838547   PRAMEF22               go_biological_process   \n",
       "\n",
       "                                                   geneset  \n",
       "0                     BOYAULT_LIVER_CANCER_SUBCLASS_G56_DN  \n",
       "1                                       ELVIDGE_HYPOXIA_UP  \n",
       "2                                 NUYTTEN_NIPP1_TARGETS_DN  \n",
       "3                GSE17721_POLYIC_VS_GARDIQUIMOD_4H_BMDC_DN  \n",
       "4                   SCHAEFFER_PROSTATE_DEVELOPMENT_12HR_UP  \n",
       "...                                                    ...  \n",
       "3838543  GOBP_POSITIVE_REGULATION_OF_CELL_POPULATION_PR...  \n",
       "3838544                             GOBP_APOPTOTIC_PROCESS  \n",
       "3838545                      GOBP_REGULATION_OF_CELL_DEATH  \n",
       "3838546  GOBP_NEGATIVE_REGULATION_OF_DEVELOPMENTAL_PROCESS  \n",
       "3838547             GOBP_NEGATIVE_REGULATION_OF_CELL_DEATH  \n",
       "\n",
       "[3838548 rows x 3 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msigdb = dc.get_resource('MSigDB')\n",
    "msigdb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f181de19-6079-4524-8342-476ac46c5a20",
   "metadata": {},
   "source": [
    "As an example, we will use the hallmark gene sets, but we could have used any other. \n",
    "\n",
    "<div class=\"alert alert-info\">\n",
    "\n",
    "**Note**\n",
    "    \n",
    "To see what other collections are available in MSigDB, type: `msigdb['collection'].unique()`.\n",
    "\n",
    "</div>  \n",
    "\n",
    "We can filter by for `hallmark`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "648addb3-349c-4df3-baea-322a363bd641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genesymbol</th>\n",
       "      <th>collection</th>\n",
       "      <th>geneset</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>IL2_STAT5_SIGNALING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>COAGULATION</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>HYPOXIA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>TNFA_SIGNALING_VIA_NFKB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>377</th>\n",
       "      <td>MAFF</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>COMPLEMENT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1449668</th>\n",
       "      <td>STXBP1</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>PANCREAS_BETA_CELLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450315</th>\n",
       "      <td>ELP4</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>PANCREAS_BETA_CELLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450526</th>\n",
       "      <td>GCG</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>PANCREAS_BETA_CELLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450731</th>\n",
       "      <td>PCSK2</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>PANCREAS_BETA_CELLS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1450916</th>\n",
       "      <td>PAX6</td>\n",
       "      <td>hallmark</td>\n",
       "      <td>PANCREAS_BETA_CELLS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7318 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        genesymbol collection                  geneset\n",
       "233           MAFF   hallmark      IL2_STAT5_SIGNALING\n",
       "250           MAFF   hallmark              COAGULATION\n",
       "270           MAFF   hallmark                  HYPOXIA\n",
       "373           MAFF   hallmark  TNFA_SIGNALING_VIA_NFKB\n",
       "377           MAFF   hallmark               COMPLEMENT\n",
       "...            ...        ...                      ...\n",
       "1449668     STXBP1   hallmark      PANCREAS_BETA_CELLS\n",
       "1450315       ELP4   hallmark      PANCREAS_BETA_CELLS\n",
       "1450526        GCG   hallmark      PANCREAS_BETA_CELLS\n",
       "1450731      PCSK2   hallmark      PANCREAS_BETA_CELLS\n",
       "1450916       PAX6   hallmark      PANCREAS_BETA_CELLS\n",
       "\n",
       "[7318 rows x 3 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter by hallmark\n",
    "msigdb = msigdb[msigdb['collection']=='hallmark']\n",
    "\n",
    "# Remove duplicated entries\n",
    "msigdb = msigdb[~msigdb.duplicated(['geneset', 'genesymbol'])]\n",
    "\n",
    "# Rename\n",
    "msigdb.loc[:, 'geneset'] = [name.split('HALLMARK_')[1] for name in msigdb['geneset']]\n",
    "\n",
    "msigdb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da91373b-d21a-4f17-9af3-bfcfb6318291",
   "metadata": {},
   "source": [
    "### Enrichment with Over Representation Analysis (ORA)\n",
    "\n",
    "To infer functional enrichment scores we will run the Over Representation Analysis (`ora`) method.\n",
    "As input data it accepts an expression matrix (`decoupler.run_ora`) or the results of differential expression analysis (`decoupler.run_ora_df`).\n",
    "For the former, by default the top 5% of expressed genes by sample are selected as the set of interest (S*), and for the latter a user-defined\n",
    "significance filtering can be used.\n",
    "Once we have S*, it builds a contingency table using set operations for each set stored in the gene set resource being used (`net`).\n",
    "Using the contingency table, `ora` performs a one-sided Fisher exact test to test for significance of overlap between sets.\n",
    "The final score is obtained by log-transforming the obtained p-values, meaning that higher values are more significant.\n",
    "\n",
    "<img src=\"../ora.png\" />\n",
    "     \n",
    "We can run `ora` with a simple one-liner:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "372e0583",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADIPOGENESIS</th>\n",
       "      <th>ALLOGRAFT_REJECTION</th>\n",
       "      <th>ANDROGEN_RESPONSE</th>\n",
       "      <th>ANGIOGENESIS</th>\n",
       "      <th>APICAL_JUNCTION</th>\n",
       "      <th>APICAL_SURFACE</th>\n",
       "      <th>APOPTOSIS</th>\n",
       "      <th>BILE_ACID_METABOLISM</th>\n",
       "      <th>CHOLESTEROL_HOMEOSTASIS</th>\n",
       "      <th>COAGULATION</th>\n",
       "      <th>...</th>\n",
       "      <th>PROTEIN_SECRETION</th>\n",
       "      <th>REACTIVE_OXYGEN_SPECIES_PATHWAY</th>\n",
       "      <th>SPERMATOGENESIS</th>\n",
       "      <th>TGF_BETA_SIGNALING</th>\n",
       "      <th>TNFA_SIGNALING_VIA_NFKB</th>\n",
       "      <th>UNFOLDED_PROTEIN_RESPONSE</th>\n",
       "      <th>UV_RESPONSE_DN</th>\n",
       "      <th>UV_RESPONSE_UP</th>\n",
       "      <th>WNT_BETA_CATENIN_SIGNALING</th>\n",
       "      <th>XENOBIOTIC_METABOLISM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>treatment.vs.control</th>\n",
       "      <td>7.529597e-41</td>\n",
       "      <td>3.818284e-24</td>\n",
       "      <td>2.010532e-21</td>\n",
       "      <td>1.677850e-07</td>\n",
       "      <td>9.410098e-37</td>\n",
       "      <td>7.686361e-08</td>\n",
       "      <td>2.064548e-36</td>\n",
       "      <td>1.054623e-18</td>\n",
       "      <td>2.105839e-20</td>\n",
       "      <td>1.744101e-24</td>\n",
       "      <td>...</td>\n",
       "      <td>2.010532e-21</td>\n",
       "      <td>6.268350e-13</td>\n",
       "      <td>1.153058e-16</td>\n",
       "      <td>6.008191e-14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.966153e-25</td>\n",
       "      <td>4.288777e-37</td>\n",
       "      <td>2.835032e-30</td>\n",
       "      <td>3.249205e-10</td>\n",
       "      <td>9.936055e-36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      ADIPOGENESIS  ALLOGRAFT_REJECTION  ANDROGEN_RESPONSE  \\\n",
       "treatment.vs.control  7.529597e-41         3.818284e-24       2.010532e-21   \n",
       "\n",
       "                      ANGIOGENESIS  APICAL_JUNCTION  APICAL_SURFACE  \\\n",
       "treatment.vs.control  1.677850e-07     9.410098e-37    7.686361e-08   \n",
       "\n",
       "                         APOPTOSIS  BILE_ACID_METABOLISM  \\\n",
       "treatment.vs.control  2.064548e-36          1.054623e-18   \n",
       "\n",
       "                      CHOLESTEROL_HOMEOSTASIS   COAGULATION  ...  \\\n",
       "treatment.vs.control             2.105839e-20  1.744101e-24  ...   \n",
       "\n",
       "                      PROTEIN_SECRETION  REACTIVE_OXYGEN_SPECIES_PATHWAY  \\\n",
       "treatment.vs.control       2.010532e-21                     6.268350e-13   \n",
       "\n",
       "                      SPERMATOGENESIS  TGF_BETA_SIGNALING  \\\n",
       "treatment.vs.control     1.153058e-16        6.008191e-14   \n",
       "\n",
       "                      TNFA_SIGNALING_VIA_NFKB  UNFOLDED_PROTEIN_RESPONSE  \\\n",
       "treatment.vs.control                      0.0               7.966153e-25   \n",
       "\n",
       "                      UV_RESPONSE_DN  UV_RESPONSE_UP  \\\n",
       "treatment.vs.control    4.288777e-37    2.835032e-30   \n",
       "\n",
       "                      WNT_BETA_CATENIN_SIGNALING  XENOBIOTIC_METABOLISM  \n",
       "treatment.vs.control                3.249205e-10           9.936055e-36  \n",
       "\n",
       "[1 rows x 50 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Infer enrichment with ora using significant deg\n",
    "top_genes = results_df[results_df['padj'] < 0.05].reset_index()\n",
    "top_genes['group'] = 'treatment.vs.control'\n",
    "\n",
    "# Run ora\n",
    "enr_pvals = dc.get_ora_df(\n",
    "    df=top_genes,\n",
    "    net=msigdb,\n",
    "    groupby='group',\n",
    "    features='GeneName',\n",
    "    source='geneset',\n",
    "    target='genesymbol'\n",
    ")\n",
    "\n",
    "enr_pvals"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73c5d319",
   "metadata": {},
   "source": [
    " We can then transform these p-values to their -log10 (so that the higher the value, the more significant the p-value).\n",
    " We will also set 0s to a minimum p-value so that we do not get infinites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b7f8e504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set 0s to min p-value\n",
    "enr_pvals.values[enr_pvals.values == 0] = np.min(enr_pvals.values[enr_pvals.values != 0])\n",
    "\n",
    "# Log-transform\n",
    "enr_pvals = -np.log10(enr_pvals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c66b649",
   "metadata": {},
   "source": [
    "Then we can visualize the most enriched terms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "bd4a91d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_barplot(enr_pvals, 'treatment.vs.control', top=25, vertical=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c34d80d",
   "metadata": {},
   "source": [
    "TNFa and interferons response (JAK-STAT) processes seem to be enriched. We previously observed a similar result with the PROGENy pathways, were they were significantly downregulated. Therefore, one of the limitations of using a prior knowledge resource without weights is that it doesn't provide direction."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66a1a653-5df2-4017-bc90-bd24bfd92e7f",
   "metadata": {},
   "source": [
    "## Enrichment of ligand-receptor interactions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97485bba-b413-4158-8941-a1cd19443295",
   "metadata": {},
   "source": [
    "Recently, study of interactions between ligands and receptors have gained significant traction, notably pushed by the democratization of single cell sequencing technologies. While most methods (such as the one described in LIANA) are developed for single cell datasets, they rely on a relatively simple assumption of co-expression or co-regulation of two (or more in the context of complexes) genes acting as ligand and receptors to propose hypothetical ligand-receptor interaction events. This concept can seamlessly be applied to a bulk RNA dataset, where the assumption is that sender and receiver cells are convoluted in a single dataset, but the observation of significant co-regulation of ligand and receptors should still correspond to hypothetical ligand-receptor interaction events.\n",
    "\n",
    "At the core, most current ligand-receptor interaction methods rely on averaging the measurements obtained for ligand and receptor and standardizing them against a background distribution.  Thus, an enrichment method based on a weighted mean can emulate this, where the sets are simply the members of a ligand receptor pair (or more in the context of complexes).\n",
    "\n",
    "Thus, we can extract ligand-receptor interaction ressources from the LIANA package (available both in [R](https://saezlab.github.io/liana/) and [python](https://liana-py.readthedocs.io/en/latest/)), and use it as a prior knowledge network with decoupler to find the most significant pairs of ligand-receptors in a given bulk dataset.\n",
    "\n",
    "While more work is required to fully understand the functional relevance of the highlighted ligand-receptor interactions with such an approach, this represents a very straightforward and intuitive approach to embed a bulk RNA dataset with ligand-receptor interaction prior knowledge.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cadf02c-e62a-44e7-b03d-1174afbd4a98",
   "metadata": {},
   "source": [
    "First, we extract ligand-receptor interactions from liana, and decomplexify them to format them into an appropriate decoupleR input."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8782c7fa-dc4e-445b-a57c-a51d24164155",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>interaction</th>\n",
       "      <th>genes</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LGALS9|PTPRC</td>\n",
       "      <td>LGALS9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LGALS9|MET</td>\n",
       "      <td>LGALS9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>LGALS9|CD44</td>\n",
       "      <td>LGALS9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>LGALS9|LRP1</td>\n",
       "      <td>LGALS9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LGALS9|CD47</td>\n",
       "      <td>LGALS9</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5849</th>\n",
       "      <td>BMP2|ACTR2</td>\n",
       "      <td>ACTR2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5850</th>\n",
       "      <td>BMP15|ACTR2</td>\n",
       "      <td>ACTR2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5851</th>\n",
       "      <td>CSF1|CSF3R</td>\n",
       "      <td>CSF3R</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5852</th>\n",
       "      <td>IL36G|IFNAR1</td>\n",
       "      <td>IFNAR1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5853</th>\n",
       "      <td>IL36G|IFNAR2</td>\n",
       "      <td>IFNAR2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10532 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       interaction   genes  weight\n",
       "0     LGALS9|PTPRC  LGALS9       1\n",
       "1       LGALS9|MET  LGALS9       1\n",
       "2      LGALS9|CD44  LGALS9       1\n",
       "3      LGALS9|LRP1  LGALS9       1\n",
       "4      LGALS9|CD47  LGALS9       1\n",
       "...            ...     ...     ...\n",
       "5849    BMP2|ACTR2   ACTR2       1\n",
       "5850   BMP15|ACTR2   ACTR2       1\n",
       "5851    CSF1|CSF3R   CSF3R       1\n",
       "5852  IL36G|IFNAR1  IFNAR1       1\n",
       "5853  IL36G|IFNAR2  IFNAR2       1\n",
       "\n",
       "[10532 rows x 3 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import liana as ln\n",
    "\n",
    "liana_lr = ln.resource.select_resource()\n",
    "liana_lr = ln.resource.explode_complexes(liana_lr)\n",
    "\n",
    "# Create two new DataFrames, each containing one of the pairs of columns to be concatenated\n",
    "df1 = liana_lr[['interaction', 'ligand']]\n",
    "df2 = liana_lr[['interaction', 'receptor']]\n",
    "\n",
    "# Rename the columns in each new DataFrame\n",
    "df1.columns = ['interaction', 'genes']\n",
    "df2.columns = ['interaction', 'genes']\n",
    "\n",
    "# Concatenate the two new DataFrames\n",
    "liana_lr = pd.concat([df1, df2], axis=0)\n",
    "liana_lr['weight'] = 1\n",
    "\n",
    "# Find duplicated rows\n",
    "duplicates = liana_lr.duplicated()\n",
    "\n",
    "# Remove duplicated rows\n",
    "liana_lr = liana_lr[~duplicates]\n",
    "\n",
    "liana_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d440fc-04d8-44c8-b29c-5bb3fa8f3900",
   "metadata": {},
   "source": [
    "Then we can use `ulm` to find the significant co-regulated pairs of ligand and receptors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0fd5af26-a8a7-42c3-a472-3228be499faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running ulm on mat with 1 samples and 17575 targets for 1854 sources.\n"
     ]
    }
   ],
   "source": [
    "# Infer lr activities with ulm\n",
    "lr_score, lr_pvalue = dc.run_ulm(\n",
    "    mat=mat,\n",
    "    net=liana_lr,\n",
    "    source='interaction',\n",
    "    target='genes',\n",
    "    min_n=2,\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d661b84d-d87c-492d-8f0f-aadc66a445e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_barplot(lr_score, 'treatment.vs.control', top=25, vertical=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb7c3f3f-555d-47d0-a55c-de23dc54a3f2",
   "metadata": {},
   "source": [
    "Interactions between colagens and the ITG_ITG complexes seems to be quite enrichned. That is especially relevant since the EMT pathway was significantly enriched in the functional pathway analysis."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dcp39",
   "language": "python",
   "name": "dcp39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.15"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
